Intermittent Learning: On-Device Machine Learning on Intermittently Powered System

With the emergence of batteryless computing platforms, we are now able to execute computer programs on embedded systems that do not require a dedicated energy source. These platforms are typically used in sensing applications [30, 39, 70, 73, 79], and their hardware architecture consists primarily of a sensor-enabled microcontroller that is powered by some form of harvested energy such as solar, RF or piezoelectric [63]. Programs that run on these platforms follow the so-called intermittent computing paradigm [50, 52, 75, 77] where a system pauses and resumes its code execution based on the availability of harvested energy. Over the past decade, the efficiency of batteryless computing platforms has been improved by reducing their energy waste through hardware provisioning, through check-pointing [64] to avoid restarting code execution from the beginning at each power-up [8], and through discarding stale sensor data [34] which are no longer useful. Despite these advancements, the capability of batteryless computing platforms has remained limited to simple sensing applications only. In this paper, we introduce the concept of intermittent learning (Figure 1) which makes energy harvested embedded systems capable of executing lightweight machine learning tasks. Their ability to run machine learning tasks inside energy harvesting microcontrollers pushes the boundary of batteryless computing as these devices are able to sense, learn, infer, and evolve over a prolonged lifetime. The proposed intermittent learning paradigm enables a true lifelong learning experience in mobile and embedded systems and advances sensor systems from being smart to smarter. Once deployed in the field, an intermittent learner classifies sensor data as well as learns from them to update the classifier at run-time--without requiring any help from any external system.